Where does your field service organization fall on the analytics usage model?

Want to improve service outcomes, but not sure where to start? Take a look at how incoming service data can improve strategic decision-making.

Ray Thomas

04/13/2015

Share

In any business, it’s important to measure success rates and continually be thinking of ways to improve. As the saying goes: “The 7 most expensive words in business today are: ‘We have always done it that way.’”

Yet, even with the advanced data-analytics technology available today, many businesses still don’t take advantage of smart, data-backed intelligence to guide decisions.

With an operation as complex as field service—with work orders, parts, scheduling, tasks, fleets, labor time, billing, and much more—organizations are driving blind without the tools and metrics to measure their success in each area.

Knowing the huge improvements data analytics can make in your organization, we created these profiles to help you determine where your business falls on the service analytics usage model:

All-In – “We look at data about our service business every day and adjust operations based on metrics. People are held accountable to specific targets and rewarded accordingly.”

Service businesses that are “all-in” for data analytics have both the means to collect key information in the field, like technician performance, customer satisfaction, or competitor details, and make sense of that data through detailed reporting and analytics. They’re empowered to diagnose issues quicker, invest in a predictive and proactive service model, and engage employees based on real-time performance reviews.

Believers – “We look at some reports and metrics today on a periodic basis. It shapes where we invest in a broad sense but not much in a day to day sense.”

Data believers get it. They prioritize data collection in the field and have a way to visualize and analyze that information in the back office. They’re almost there, but instead of using the wealth of information they gain to inform day-to-day decisions and make changes, they use it only to benchmark current strategies. While measuring and benchmarking is great, it’s not enough to make real forward-thinking improvements in the business.

One foot in – “We are just getting started or are looking at a system to help us with this.”

Service companies with “one foot in” are convinced that data analytics could have an impact on their business, but may not have a concrete strategy or the tools in place to help them get there. Their next steps are finding a data analytics solution to meet their business needs, mapping out how they’d like to transition their current data into an automated system, and, once everything’s set up, making informed decisions based on the flow of new data coming in.

Skeptics – “I’m willing to look into this but I’m not sure how much value it could provide.”

Skeptics need a little convincing to get on board the data analytics train. They haven’t ruled it out, but they’re unsure of the benefits. Maybe they don’t want to deal with the hassle of transitioning manual service processes to an automated system. Or maybe they’re unsure what data to collect and how to organize it, so they don’t know where to start.

Nonbelievers – “I’m not going to invest in this area, ever. It’s not relevant to improving my operations.”

Nonbelievers may be in trouble. If you work for an organization full of nonbelievers, you may want to consider either getting out or trying to convert them into believers. Businesses that ignore the value of actionable, data-driven insights are flying blind and could very well be flying your service organization straight into the ground.

Where do you fall on the spectrum?

If you’re a skeptic or a nonbeliever, what are your reasons? Do you really think your business is better off without the access, action, and visibility to strengthen executive decision making? Or are you worried about the need for change and current inefficiencies that detailed data analytics may reveal?

With a growing list of pressures facing service executives, organizations can benefit from advanced data analytics to achieve better results with access to timely information and to inform decision making.

Beyond Big Data: converting information into intelligence

Of course, service businesses need tools to collect data in the field and advanced analytics to help tell the whole story behind their service operations, but it’s not just about possessing quality data. Service organizations’ full potential comes from the action stemming from those data insights.

Once you have a system running which allows your data to work for you, service organizations can leverage smart data to inform executive decisions, benchmark your performance against competitors, align incentives with metrics, and share real-time intelligence with customers.

Are you all-in for service analytics?

Do your field techs have the ability to collect key service data in the field? How about the tools to visualize and analyze that data in the back office? Going all-in for service analytics can mean the difference between moving forward and making the same old mistakes.

-Ray Thomas is is the VP of Sales for MSI Data. This article originally appeared here. Edited by Anisa Samarxhiu, digital project manager, Control Engineering, asamarxhiu@cfemedia.com.

This article collection contains several articles on how advancements in vision system designs, computing power, algorithms, optics, and communications are making machine vision more cost effective than ever before.

Programmable logic controllers (PLCs) represent the logic (decision) part of the control loop of sense, decide, and actuate. As we know, PLCs aren’t the only option for making decisions in a control loop, but they are likely why you’re here.

This article collection contains several articles on how advancements in vision system designs, computing power, algorithms, optics, and communications are making machine vision more cost effective than ever before.

Programmable logic controllers (PLCs) represent the logic (decision) part of the control loop of sense, decide, and actuate. As we know, PLCs aren’t the only option for making decisions in a control loop, but they are likely why you’re here.

This article collection contains several articles on how advancements in vision system designs, computing power, algorithms, optics, and communications are making machine vision more cost effective than ever before.

Programmable logic controllers (PLCs) represent the logic (decision) part of the control loop of sense, decide, and actuate. As we know, PLCs aren’t the only option for making decisions in a control loop, but they are likely why you’re here.